How is this calculated?
The math continuously tracks how strongly this post is connected to the rest of the network. Every tag forms a 2-way link. The base stats determine personal node strength, and the pie charts below show this node's share against its direct neighbours.
// 1. Base variables (floored at 1 to prevent zero-multiplication math errors)
$inbound = max(1, 5) = 5
$outbound = max(1, 5) = 5
// 2. Node Base Values (Local connection strength)
Base_Strength (PV) = $inbound * $outbound = 5 * 5 = 25
Base_Influence (IV) = $inbound / $outbound = 5 / 5 = 1
// 3. Exponential Network Values (accumulating 23 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
= 25 *
( 2 [London Underground electric locomotives] *
256 [London Underground] *
289 [London Underground rolling stock] *
36 [British Rail tube trains] *
1 [Drawings of London Underground rolling stock] *
4 [London Underground battery locomotives] *
1 [London Underground coaches] *
1 [London Underground electric multiple units] *
36 [London Underground engineering stock] *
1 [London Underground roundels on rolling stock] *
1 [London Underground steam locomotives] *
1 [London Underground train interiors] *
1 [Trains at London Underground stations] *
1 [London Underground sub-surface and tube train comparison images] *
1 [Models of London Underground rolling stock] *
1 [Train liveries of London Underground] *
1 [Trains of London Underground by line] *
1 [British Rail Class 66s in Metronet livery] *
1 [Schöma CFL 500 VR] *
1 [British Rail Class 20s of London Underground] *
1 [London Underground Asset Inspection Train] *
1 [London Underground ex-GWR 5700 Class 0-6-0PTs] *
1 [London Underground Track Recording Train]
)
= 19.18B
Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
= 1 *
( 2 [London Underground electric locomotives] *
1 [London Underground] *
1 [London Underground rolling stock] *
1 [British Rail tube trains] *
1 [Drawings of London Underground rolling stock] *
1 [London Underground battery locomotives] *
1 [London Underground coaches] *
1 [London Underground electric multiple units] *
1 [London Underground engineering stock] *
1 [London Underground roundels on rolling stock] *
1 [London Underground steam locomotives] *
1 [London Underground train interiors] *
1 [Trains at London Underground stations] *
1 [London Underground sub-surface and tube train comparison images] *
1 [Models of London Underground rolling stock] *
1 [Train liveries of London Underground] *
1 [Trains of London Underground by line] *
1 [British Rail Class 66s in Metronet livery] *
1 [Schöma CFL 500 VR] *
1 [British Rail Class 20s of London Underground] *
1 [London Underground Asset Inspection Train] *
1 [London Underground ex-GWR 5700 Class 0-6-0PTs] *
1 [London Underground Track Recording Train]
)
= 2
Outbound
5
Tags on post
Inbound
5
Posts tagging this
Connections
23
Total nodes
Base Node Strength
25
Base Node Influence
1
Strength Share (vs Direct Neighbours)
Dominant nodes (excluded from chart)London Underground rolling stock 43.46%London Underground 38.50%British Rail tube trains 5.41%London Underground engineering stock 5.41%
Influence Share (vs Direct Neighbours)
Connected Network Hierarchy
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Connection Health Audit (Red = broken 1-way link)